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At the edge of the network close to the source of the data, edge computing deploys computing, storage and other capabilities to provide intelligent services in close proximity and offers low bandwidth consumption, low latency and high security. It satisfies the requirements of transmission bandwidth, real-time and security for Internet of Things (I...
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... there is no unified academic standard for exploring the architecture of EC-IoT and there needs to be more relevant technologies and specific applications regarding EC-IoT solutions. As shown in Figure 1, from the application of edge computing in the IoT field, the development of IoT edge computing is comprehensively studied through the EC-IoT architecture. It is hoped to bring inspiration to the researchers and enterprises engaged in the field of EC-IoT. ...
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The Internet of Things (IoT) refers to physical objects with sensors, computing power, software, and other technologies that communicate and exchange data with other devices, platforms, and systems over the Internet or other communication networks. Remarkable developments in IoT have paved the way for new possibilities, enabling the creation and au...
Citations
... In [89], reference architectures are discussed for industrial IoT, Internet of Vehicles (IoV) as well as IoT-based smart homes. In [90], a novel approach is introduced that adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand, and delay sensitivity) as well as resource utilization and resource heterogeneity. ...
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.
... An IoT edge computing (EC-IoT) reference design with three layers-the network edge, the end edge, and the cloud edge-is proposed in the article [16]. Examining the key AI application technologies in the EC-IoT reference architecture. ...
This research used a wearable sensor to gather photoplethysmography (PPG) signals from 15 healthy subjects. The dataset includes 7,308 PPG segments, each containing 8 seconds of PPG data and corresponding labels indicating the type of physical activity the subject performed. The article proposes a convolutional neural network (CNN) model to classify physical activity from the PPG signals. The proposed model includes several layers: batch normalization, convolutional, max-pooling, dropout, and fully connected. The output layer uses the softmax activation function to compute the probabilities of each class. Regarding performance, the suggested CNN model outperforms conventional models like SVM with RBF kernel, Decision Tree, and Random Forest. The article also suggests several techniques to optimize the model further, which can be beneficial for developing IoMT applications such as activity recognition and vital signs monitoring.
... By 2025, the International Data Corporation (IDC) estimates there will be 150 billion smart edge devices available worldwide [47]. Although some kinds of edge computing are currently in use, analysts predict that this volume will increase [48]. There has been remarkable advancement in the use of artificial intelligence (AI), instead of heuristic and meta-heuristic methods, to enhance task scheduling [49]. ...
... When it comes to AI, edge computing is often thought of as the "final mile" because of the autonomous installation of smart services and on-edge nodes. Large numbers of edge devices (miniaturized, distributed, and reduced-power) can implement precise AI or, in coordination with other devices, for a variety of uses, such as networks of IoT nodes [48]. The intelligence for such services can be distributed to the edge to handle the task offloading challenge and satisfy the reliability and minimized-latency needs of data transfer over networks. ...
The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.
... It is a collection of various hardware and software agents. Although there are quite a lot of IoT architectures proposed recently in [25][26][27], they have no integrated means for explainability included. The following method complements existing IoT architecture models by integrating an explanation interface. ...
The technological maturity of AI solutions has been consistently increasing over the years, expanding its application scope and domains. Smart home systems have evolved to act as proactive assistants for their residents, autonomously detecting behavioral patterns, inferring needs, and making decisions pertaining to the management and control of various home subsystems. The implementation of explainable AI (XAI) solutions in this challenging domain can improve user experience and trust by providing clear and understandable explanations of the system’s behavior. The article discusses the increasing importance of explainable artificial intelligence (XAI) in smart home systems, which are becoming progressively smarter and more accessible to end-users, and presents an agent-based approach for developing explainable Internet of things (IoT) systems and an experiment conducted at the Centre of Real Time Computer Systems at the Kaunas University of Technology. The proposed method was adapted to build an explainable, rule-based smart home system for controlling light, heating, and ventilation. The results of this study serve as a demonstration of the feasibility and effectiveness of the proposed theoretical approach in real-world scenarios.
In this study, the p-type Zinc Telluride (ZnTe) thin films were deposited by RF magnetron sputtering technique on the patterned-ITO substrates. The RF-sputtered p-type ZnTe thin films having 201, 308, 362 and 457 nm thicknesses have been characterized before device fabrication. The SEM and XRD analysis showed that increasing film thickness has caused cluster-like growth and the presence of ZnO grains, respectively. In addition, the EDS analysis has been used to determine the
composition of Zn and Te. The EDS spectra showed that the ZnO grains may contribute to n-type conductivity as well as to p-type conductivity due to the oxygen-rich (ZnO:O) or tellurium-doped (ZnO:Te) structures. The thickness depends root mean sequence (RMS) values of the ZnTe films are 4.60 nm, 14.36 nm, 20.10 nm, and 27.95 nm, respectively. On the other hand, the threshold voltage (Vth), subthreshold current (Ioff), subthreshold slope (SS) and field effect mobility (휇) of the p-type ZnTe thin film transistors (TFTs) have investigated depending on channel layer thickness. The increasing film thickness has caused decreasing performance parameters from 9.75 V, 6.20 × 10–10 A, 1.15 V/dec and 3,30 cm2V−1 s−1 to 2.50 V, 3.60 × 10–10 A, 0.72 V/dec and 1,68 cm2V−1 s−1, respectively. The film having 100 nm thickness has best saturation-current value.
